新老版本的结果差距

先决条件

• 我已经搜索过 问题 和 讨论 但未得到预期的帮助。
• 错误在 最新版本 中尚未被修复。

问题类型

我正在使用官方支持的任务/模型/数据集进行评估。

环境

{'CUDA available': True,
'CUDA_HOME': '/usr',
'GCC': 'gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0',
'GPU 0,1,2,3,4,5': 'NVIDIA A800 80GB PCIe',
'MMEngine': '0.10.4',
'MUSA available': False,
'NVCC': 'Cuda compilation tools, release 10.1, V10.1.24',
'OpenCV': '4.9.0',
'PyTorch': '2.3.0+cu121',
'PyTorch compiling details': 'PyTorch built with:\n'
...

重现问题 - 代码/配置示例

configs/models 下面的配置都是
api_meta_template = dict(
round=[
dict(role="HUMAN", api_role="HUMAN"),
dict(role="BOT", api_role="BOT", generate=True),
],
)

models = [
dict(
abbr="vanilla_llama-2-7b-chat_V1",
# type=Llama2Chat,
type=HuggingFaceCausalLM,
path="xxx",
tokenizer_path="xxx",
tokenizer_kwargs=dict(padding_side='left',
truncation_side='left',
use_fast=False,
),
meta_template=api_meta_template,
max_out_len=100,
max_seq_len=2048,
batch_size=8,
extract_pred_after_decode = True,
model_kwargs=dict(device_map='auto'),
batch_padding=False, # if false, inference with for-loop without batch padding
run_cfg=dict(num_gpus=1, num_procs=1),
),
]

重现问题 - 命令或脚本

python run.py --models llama --datasets triviaqa_gen

重现问题 - 错误信息

我使用去年大概10月份下载的版本，和现在的版本运行triviaqa数据集得到的结果差距较大

outputs得到的configs下面的.py文件如下

**(1) triviaqa **
原始版本 结果为45
datasets=[
dict(abbr='triviaqa',
eval_cfg=dict(
evaluator=dict(
type='opencompass.datasets.TriviaQAEvaluator'),
pred_role='BOT'),
infer_cfg=dict(
inferencer=dict(
max_out_len=50,
type='opencompass.openicl.icl_inferencer.GenInferencer'),
prompt_template=dict(
template=dict(
round=[
dict(prompt="Answer these questions, your answer should be as simple as possible, start your answer with the prompt 'The answer is '.\nQ: {question}?",
role='HUMAN'),
dict(prompt='A:',
role='BOT'),
]),
type='opencompass.openicl.icl_prompt_template.PromptTemplate'),
retriever=dict(
type='opencompass.openicl.icl_retriever.ZeroRetriever')),
path='./data/triviaqa/',
reader_cfg=dict(
input_columns=[
'question',
],
output_column='answer',
test_split='dev',
train_split='dev'),
type='opencompass.datasets.TriviaQADataset'),
]
models=[
dict(abbr='llama',
batch_padding=False,
batch_size=8,
extract_pred_after_decode=True,
max_out_len=100,
max_seq_len=2048,
meta_template=dict(
round=[
dict(api_role='HUMAN',
role='HUMAN'),
dict(api_role='BOT',
generate=True,
role='BOT'),
]),
model_kwargs=dict(
device_map='auto'),
path='xx',
run_cfg=dict(
num_gpus=1,
num_procs=1),
tokenizer_kwargs=dict(
padding_side='left',
truncation_side='left',
use_fast=False),
tokenizer_path='xx',
type='opencompass.models.HuggingFaceCausalLM'),
]
summarizer=None
work_dir='./outputs/default/20231012_212838'

(2)当前版本 结果为55
datasets=[
dict(abbr='triviaqa',
eval_cfg=dict(
evaluator=dict(
type='opencompass.datasets.TriviaQAEvaluator'),
pred_role='BOT'),
infer_cfg=dict(
inferencer=dict(
max_out_len=50,
type='opencompass.openicl.icl_inferencer.GenInferencer'),
prompt_template=dict(
template=dict(
round=[
dict(prompt="Answer these questions, your answer should be as simple as possible, start your answer with the prompt 'The answer is '.\nQ: {question}?",
role='HUMAN'),
dict(prompt='A:',
role='BOT'),
]),
type='opencompass.openicl.icl_prompt_template.PromptTemplate'),
retriever=dict(
type='opencompass.openicl.icl_retriever.ZeroRetriever')),
path='./data/triviaqa/',
reader_cfg=dict(
input_columns=[
'question',
],
output_column='answer',
test_split='dev',
train_split='dev'),
type='opencompass.datasets.TriviaQADataset'),
]
models=[
dict(abbr='llama',
batch_padding=False,
batch_size=8,
extract_pred_after_decode=True,
max_out_len=100,
max_seq_len=2048,
meta_template=dict(
round=[
dict(api_role='HUMAN',
role='HUMAN'),
dict(api_role='BOT',
generate=True,
role='BOT'),
]),
model_kwargs=dict(
device_map='auto'),
path='xx',
run_cfg=dict(
num_gpus=1,
num_procs=1),
tokenizer_kwargs=dict(
padding_side='left',
truncation_side='left',
use_fast=False),
tokenizer_path='xx',
type='opencompass.models.HuggingFaceCausalLM'),
]
summarizer=dict(
summary_groups=[
dict(name='agieval-chinese',
subsets=[
'agieval-gaokao-chinese',

......

work_dir='./outputs/default/20240507_115016'

其他信息

请问是什么原因导致的结果不一样？